Literature DB >> 29956130

QModeling: a Multiplatform, Easy-to-Use and Open-Source Toolbox for PET Kinetic Analysis.

Francisco J López-González1,2, José Paredes-Pacheco3,4, Karl Thurnhofer-Hemsi3,5, Carlos Rossi5, Manuel Enciso5, Daniel Toro-Flores3, Belén Murcia-Casas6, Antonio L Gutiérrez-Cardo3,7, Núria Roé-Vellvé3.   

Abstract

Kinetic modeling is at the basis of most quantification methods for dynamic PET data. Specific software is required for it, and a free and easy-to-use kinetic analysis toolbox can facilitate routine work for clinical research. The relevance of kinetic modeling for neuroimaging encourages its incorporation into image processing pipelines like those of SPM, also providing preprocessing flexibility to match the needs of users. The aim of this work was to develop such a toolbox: QModeling. It implements four widely-used reference-region models: Simplified Reference Tissue Model (SRTM), Simplified Reference Tissue Model 2 (SRTM2), Patlak Reference and Logan Reference. A preliminary validation was also performed: The obtained parameters were compared with the gold standard provided by PMOD, the most commonly-used software in this field. Execution speed was also compared, for time-activity curve (TAC) estimation, model fitting and image generation. QModeling has a simple interface, which guides the user through the analysis: Loading data, obtaining TACs, preprocessing the model for pre-evaluation, generating parametric images and visualizing them. Relative differences between QModeling and PMOD in the parameter values are almost always below 10-8. The SRTM2 algorithm yields relative differences from 10-3 to 10-5 when [Formula: see text] is not fixed, since different, validated methods are used to fit this parameter. The new toolbox works efficiently, with execution times of the same order as those of PMOD. Therefore, QModeling allows applying reference-region models with reliable results in efficient computation times. It is free, flexible, multiplatform, easy-to-use and open-source, and it can be easily expanded with new models.

Keywords:  Kinetic analysis; PET; Parametric images; Patlak; QModeling; SRTM

Mesh:

Year:  2019        PMID: 29956130     DOI: 10.1007/s12021-018-9384-y

Source DB:  PubMed          Journal:  Neuroinformatics        ISSN: 1539-2791


  29 in total

1.  COMKAT: compartment model kinetic analysis tool.

Authors:  R F Muzic; S Cornelius
Journal:  J Nucl Med       Date:  2001-04       Impact factor: 10.057

2.  Noise reduction in the simplified reference tissue model for neuroreceptor functional imaging.

Authors:  Yanjun Wu; Richard E Carson
Journal:  J Cereb Blood Flow Metab       Date:  2002-12       Impact factor: 6.200

3.  Requirements and implementation of a flexible kinetic modeling tool.

Authors:  C Burger; A Buck
Journal:  J Nucl Med       Date:  1997-11       Impact factor: 10.057

4.  Parametric imaging of ligand-receptor binding in PET using a simplified reference region model.

Authors:  R N Gunn; A A Lammertsma; S P Hume; V J Cunningham
Journal:  Neuroimage       Date:  1997-11       Impact factor: 6.556

5.  MANIA-a pattern classification toolbox for neuroimaging data.

Authors:  Dominik Grotegerd; Ronny Redlich; Jorge R C Almeida; Mona Riemenschneider; Harald Kugel; Volker Arolt; Udo Dannlowski
Journal:  Neuroinformatics       Date:  2014-07

6.  Estimation of serotonin transporter parameters with 11C-DASB in healthy humans: reproducibility and comparison of methods.

Authors:  W Gordon Frankle; Mark Slifstein; Roger N Gunn; Yiyun Huang; Dah-Ren Hwang; E Ashlie Darr; Rajesh Narendran; Anissa Abi-Dargham; Marc Laruelle
Journal:  J Nucl Med       Date:  2006-05       Impact factor: 10.057

7.  Comparability of [18F]THK5317 and [11C]PIB blood flow proxy images with [18F]FDG positron emission tomography in Alzheimer's disease.

Authors:  Elena Rodriguez-Vieitez; Antoine Leuzy; Konstantinos Chiotis; Laure Saint-Aubert; Anders Wall; Agneta Nordberg
Journal:  J Cereb Blood Flow Metab       Date:  2016-07-20       Impact factor: 6.200

8.  Distribution volume ratios without blood sampling from graphical analysis of PET data.

Authors:  J Logan; J S Fowler; N D Volkow; G J Wang; Y S Ding; D L Alexoff
Journal:  J Cereb Blood Flow Metab       Date:  1996-09       Impact factor: 6.200

9.  Revisiting the Logan plot to account for non-negligible blood volume in brain tissue.

Authors:  Martin Schain; Patrik Fazio; Ladislav Mrzljak; Nahid Amini; Nabil Al-Tawil; Cheryl Fitzer-Attas; Juliana Bronzova; Bernhard Landwehrmeyer; Christina Sampaio; Christer Halldin; Andrea Varrone
Journal:  EJNMMI Res       Date:  2017-08-18       Impact factor: 3.138

10.  Pharmacokinetic modeling of [11C]flumazenil kinetics in the rat brain.

Authors:  Isadora Lopes Alves; David Vállez García; Andrea Parente; Janine Doorduin; Rudi Dierckx; Ana Maria Marques da Silva; Michel Koole; Antoon Willemsen; Ronald Boellaard
Journal:  EJNMMI Res       Date:  2017-02-22       Impact factor: 3.138

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  1 in total

1.  68Ga-NOTA PET imaging for gastric emptying assessment in mice.

Authors:  Xueyan Chen; Yu Liu; Donghui Pan; Maoyu Cao; Xinyu Wang; Lizhen Wang; Yuping Xu; Yan Wang; Junjie Yan; Juan Liu; Min Yang
Journal:  BMC Gastroenterol       Date:  2021-02-13       Impact factor: 3.067

  1 in total

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